To assure the continued safe, reliable and efficient operation of a power plant, it is essential that accurate on-line information about the current state of the equipment be available to the operators. Such information is needed to determine the operability of safety and control systems, the condition of active equipment, the necessity of preventive maintenance, and the status of sensory systems.
Products useful for determining or monitoring the condition or remaining useful life of productive assets, including but not limited to power plant equipment, most often perform this surveillance function by evaluating signal or data values obtained during asset operation. One means for determining or monitoring the condition of an asset involves estimating the expected data values and comparing the estimated values to current data values obtained from the asset. When the estimated data values characterize the desired or expected operation of the asset, a disagreement between the estimated data values and the current data values provides a sensitive and reliable indication of an asset degradation or fault condition and can further provide an indication of the particular cause and severity of the asset degradation or fault. The disagreement between each estimated data value and each current data value can be computed as the numerical difference between them. This difference is often referred to as a residual data value. The residual data values, the current data values, or the estimated data values can be used to determine condition of the asset and to identify or diagnose asset degradation or fault conditions.
One means for estimating the expected data values used for determining or monitoring the condition of an asset involves the use of machine learning to calibrate (train) a model representative of the normal operation of the monitored asset. A shortcoming in the prior application of machine learning is the need to calibrate or train the model of normal operation prior to its use for on-line monitoring. The calibrated model then remains static during on-line monitoring operations. Often, asset aging changes or operating condition changes cause a statically calibrated model to eventually estimate poorly the expected data values. When the poorly estimated expected data values are then compared to current data values obtained from the asset during on-line monitoring, false alarms typically result. Currently, this problem plagues all known power industry deployments of empirical models developed by machine learning and used to determine condition of an asset or to identify or diagnose asset degradation or fault conditions over any substantial period of monitoring.
For the foregoing reasons, there is a need to overcome the significant shortcomings of the known prior-art as delineated hereinabove.